Judgment and Decision Making, Vol. 15, No. 2, March 2020, pp. 254–265

Training choices toward low value options

Michael J. Zoltak∗ Rob W. Holland† Niels Kukken‡ Harm Veling§

Abstract

Food decisions are driven by differences in value of choice alternatives such that high value items are preferred over low

value items. However, recent research has demonstrated that by implementing the Cue-Approach Training (CAT) the odds

of choosing low value items over high value items can be increased. This effect was explained by increased attention to the

low value items induced by CAT. Our goal was to replicate the original findings and to address the question of the underlying

mechanism by employing eye-tracking during participants’ choice making. During CAT participants were presented with

images of food items and were instructed to quickly respond to some of them when an auditory cue was presented (cued items),

and not without this cue (uncued items). Next, participants made choices between two food items that differed on whether

they were cued during CAT (cued versus uncued) and in pre-training value (high versus low). As predicted, results showed

participants were more likely to select a low value food item over a high value food item for consumption when the low value

food item had been cued compared to when the low value item had not been cued. Important, and against our hypothesis, there

was no significant increase in gaze time for low value cued items compared to low value uncued items. Participants did spend

more time fixating on the chosen item compared to the unchosen alternative, thus replicating previous work in this domain.

The present research thus establishes the robustness of CAT as means of facilitating choices for low value over high value food

but could not demonstrate that this increased preference was due to increased attention for cued low value items. The present

research thus raises the question how CAT may increase choices for low value options.

Keywords: cue-approach training, behaviour change, food choice, value, attention

1 Introduction

In recent years the importance of developing new ways of

modifying food choice has become increasingly clear, as the

rates of obesity and obesity related diseases have skyrock-

eted across the world (Malik, Willett & Hu, 2012). There

appears a need for developing new methods to change dietary

choices, as merely educating the populous seems to not pro-

duce behaviour change (Marteau, Hollands & Fletcher, 2012;

Wood & Neal, 2007). Research over the past decade has pro-

vided us with insight into the underlying processes of food

consumption, demonstrating that food consumption is often

strongly influenced by reward signals, and that the strength

of this signal can depend on, for example, the amount of

sugar or fat within a given product (Kenny, 2011; Krajbich,

Armel & Rangel, 2010; Stice, Spoor, Ng & Zald, 2009;

Copyright: © 2020. The authors license this article under the terms of

the Creative Commons Attribution 3.0 License.∗://orcid.org/0000-0002-4842-1870, Behavioural Science Institute,

Montessorilaan 3, Radboud University, 6500 HE, Nijmegen, the Nether-

lands. Email: m.zoltak@psych.ru.nl.†://orcid.org/0000-0002-6766-0869, Behavioural Science Institute,

Radboud University, Nijmegen, the Netherlands, and Faculty of Social and

Behavioural Sciences, University of Amsterdam, Amsterdam, the Nether-

lands.‡://orcid.org/0000-0003-3748-0370, Behavioural Science Institute,

Radboud University, Nijmegen, the Netherlands; and Fachbereich Psycholo-

gie, Eberhard Karls Universität Tübingen, Tübingen, Germany.§://orcid.org/0000-0002-5648-2980, Behavioural Science Institute,

Radboud University, Nijmegen, the Netherlands.

Volkow, Wang & Baler, 2011). Therefore, the simple act of

consuming a product linked with such a relatively strong re-

ward signal can reinforce subsequent consumption (Epstein,

Carr, Lin & Fletcher, 2011; Rangel, 2013). This basic pro-

cess of conditioning can result in enhanced attention towards

these food items (Nijs, Muris, Euser, & Franken, 2010),

and produce motor impulses aimed at obtaining these items

(Brooks, Cedernaes & Schiöth, 2013), which may facilitate

consumption. Hence, recent psychological research has fo-

cused on modifying immediate food-related responses (for a

review see Stice, Lawrence, Kemps, & Veling, 2016), which

have been acquired via basic learning mechanisms (Rangel,

2009). This approach can be considered a boosting approach

to behaviour change, as it aims to create training procedures

or learning tasks that people may use to change their pavlo-

vian biases toward rewarding food when they want this (in

contrast to the popular nudging approach where behaviour

change is changed via subtle environmental interventions;

Hertwig & Grüne-Yanoff, 2017).

A number of approaches to modify immediate responses

to food have been developed over the past decade, for ex-

ample linking appetitive food to aversive images (Hollands,

Prestwich & Marteau, 2011); repeatedly presenting appeti-

tive food images with no-go cues in a go/no-go task (Veling,

Chen et al., 2017; Veling, Lawrence et al., 2017), or linking

appetitive food images to avoidance responses (Becker, Jost-

mann, Wiers & Holland, 2015). Another way of influencing

food choice involves directly or indirectly manipulating at-

254

Judgment and Decision Making, Vol. 15, No. 2, March 2020 Training choices toward low value options 255

tention toward or away from food items. Choices for food

have been demonstrated to be linked to increased attention

for the chosen food items prior to the choice (Kemps &

Tiggemann, 2009; Krajbich, et al., 2010), and manipulat-

ing attention for specific items has been shown to result in

choice shifts toward the attended-to items (Armel, Beaumel

& Rangel, 2008; Krajbich & Rangel, 2011; Shimojo, Simion,

Shimojo & Scheier, 2003).

One paradigm developed to train attention toward specific

food items is the cue-approach training (CAT; Schonberg

et al., 2014). During the CAT, participants are presented

with food images on a computer screen one by one and are

instructed to press a keyboard button as quickly as possible

when they hear a tone, and before the image disappears from

the screen (after 1 s). The tone is presented only on 25%

percent of the trials and is consistently presented with some

food items (cued items), but not others (uncued items). To

keep the task challenging the onset of the tone is adjusted

such that the tone is presented later during a trial when a

response was successful and earlier when the response was

not successful.

The effect of CAT on food choice is assessed with a sub-

sequent binary food choice task in which participants are

asked to choose between one of two food items (Schonberg

et al., 2014). On experimental trials within this choice task,

choices are made between cued and uncued items matched on

value, as assessed beforehand by a willingness to pay mea-

sure. CAT produces a consistent effect, namely that cued

items are preferred over uncued items for around 60–65%

of the choices, particularly when both the cued and uncued

snack items are of high value (Bakkour et al., 2016, 2017;

Schonberg et al., 2014). The effect of CAT has also been

found on choices for fruits and vegetables (Veling, Chen

et al., 2017), and non-food stimuli (Salomon et al., 2018).

Interestingly, effects of CAT on choice last for months (Sa-

lomon et al., 2018; Schonberg et al., 2014). The increased

preference for cued items is explained by increased attention

to these items during choice, which has been shown with

eye-tracking (Schonberg et al., 2014).

In the initial experiments employing CAT cited above,

choices between items within the CAT paradigm have been

made between two items that are matched on value (e.g., high

vs. high, or low vs. low). Thus, the experiments showed

a shift in preference from one food item to another food

item of approximately equal value. Thus, one question is

whether this task can be used to shift a preference for a

low value item over a high value item. That is, all else

being equal, when people make a choice between a high and

a low value product, one would expect them to select the

high value product. Indeed, on so-called ‘filler’ trials of the

CAT studies described above (Bakkour et al., 2016, 2017;

Schonberg et al., 2014; Veling, Chen et al., 2017), high-value

items were pitted against low value items (either both cued,

or both uncued), and results from such trials have shown

that participants generally select the higher valued option on

approximately 80% of the trials. Would it be possible to

boost choices for low value items when they are cued and

pitted against uncued high value items?

We recently conducted two experiments that speak to this

question (Zoltak, Veling, Chen & Holland, 2018). In two

preregistered experiments, participants received choices be-

tween high and low value food items, and on the experimen-

tal trials the low value food item had been cued during CAT

whereas the high value item had not been cued. We com-

pared choices on these trials with a baseline in which both

the low and high value item had not been cued. We found

that participants on average preferred high to low items on

both experimental and baseline trials. More important, cue-

ing increased the odds of choosing the low value item over

the high value item compared to baseline. These results

appear surprising given the strong influence of value on de-

cision making (for a review see Vlaev, Chater, Stewart &

Brown, 2011) and the fact that participants made choices for

real consumption (rather than vignettes or artificial choice).

Apparently, training attention toward a food item can occa-

sionally override the effect of food value.

In the present experiment, we aim to replicate these find-

ings. Replication is important because the effect is surpris-

ing, as noted. Second, we examine an attention explanation

in increasing the odds of choosing cued low value items

over uncued high value items. In one of the experiments by

Schonberg et al. (2014), eye-tracking during the choice task

revealed that participants spend a larger proportion of the

total gaze time on the chosen versus unchosen item (see also

Krajbich & Rangel, 2011). More importantly, participants

also looked more towards cued items versus uncued items

even when they were not chosen. Note that these findings

concerned choices between alternatives that were matched

on value. It is unclear whether visual attention would also be

affected by CAT in choices made between low value and high

value items, because value has in itself a strong impact on

visual attention, such that people’s attention is drawn to high

value items (Anderson, Laurent & Yantis, 2011). Therefore,

we examined peoples’ gaze patterns during the choice task

in the present experiment.

2 Present research

Our current research thus has two main goals. First, a repli-

cation of our original findings in a larger sample (Zoltak et

al., 2018). Second, we have employed the use of eye tracking

during choice in order to see whether CAT shifts attention

toward cued low value items. Following our preregistration

(https://osf.io/39jnj/) we predict an increase in the probabil-

ity of choosing low value items in the cued low compared to

both uncued trials, as well as more total gaze time spent on

Judgment and Decision Making, Vol. 15, No. 2, March 2020 Training choices toward low value options 256

looking at low value items during the cued low choice trials

compared to both uncued choice trials.

2.1 Methods

2.1.1 Behavioural data

Participants. Participants were recruited via Radboud

University on-line recruitment system (SONA). Our sample

size was based on the effect obtained by Zoltak et al. (2018),

basing our power calculations on the collapsed results from

our two previous studies, and on a pair-wise t-test between

the proportion of choosing low value option compared to the

high value options within our main comparison, that is cued

low compared to both uncued. To obtain our effect at a level

of alpha = .05 with a power = 0.80, the projected sample

size needed is 44 participants. We preregistered (https://

osf.io/39jnj/) a total sample of 50 participants to leave some

room for exclusion. Due to our exclusion criterion, described

below, we excluded 3 participants before data analyses, re-

sulting in a final sample of 47 participants (10 males and 37

females, Mage = 21.79, SDage = 2.84)

Exclusion criterion. Based on Schonberg et al. (2014) and

our previous work (Zoltak et al., 2018) we decided to a-priori

exclude, from all analyses, participants who bid less than

25 cents (0.25 euro) on more than 40 items in the auction

described below.

2.1.2 Procedure

The procedure was almost identical to the one described in

Zoltak et al. (2018). Participants were asked to fast for 3

hours before coming to the lab, to ensure that the snacks pre-

sented to them in the experiment were appealing. Upon their

arrival, they were asked to complete the informed consent

form, and were demonstrated large variety of snack items

they could obtain at the end of the task. They were also

informed that after the task they would have to wait for 30

min in the laboratory and the only food that they would be

able to consume would be the snacks obtained in the experi-

ment. The aim of this was to make sure that participants bid

on items they would actually like to consume later (see also

Schonberg et al., 2014; Veling, Chen et al., 2017, Zoltak et

al., 2018).

Because of common pitfalls with eye tracker calibration,

such as the machine being unable to correctly determine the

pupil size due to eye lash length or eye lid height, we made

sure we could calibrate the eye tracker as soon as the par-

ticipant entered the room and before any experimental tasks

had been performed. In order to do so, participants’ domi-

nant eyes were determined, and the eye tracker was initially

calibrated upon them entering the experimental room. Af-

ter completing the initial calibration, participants performed

the auction task and then underwent the CAT. After the CAT

and before completing the choice task in the eye tracker, the

machine was calibrated once again, in order to ensure cor-

rectness of data recording. After the choice task, participants

performed a memory task and finally a second auction. After

completing the final auction participants viewed the snack

items they had obtained or could buy and were given the

opportunity to consume them during the 30-minute waiting

period. For an overview of the tasks see Figure 1.

Auction. Participants were asked to bid on 60 palatable

food items using the Becker-DeGroot-Marschak (BDM)

willingness to pay (WTP) measure (Becker, DeGroot &

Marschak, 1964). At the beginning of the task they were

given physical coins amounting to two euros and were in-

formed they will be able to purchase one of the food items

they have bid on for real consumption at the end of the ex-

periment. All of the 60 food items have been successfully

employed before (Veling, Chen et al., 2017, Zoltak et al.,

2018). Participants indicated their bids by using a 0 to 2

euro slider at the bottom of the screen. The entirety of the

auction was self-paced, and the next item would only appear

after a successful bid. At the end of the experiment, the com-

puter program would randomly pick one auction trial out of

set of available items in the lab and generate a fully random

bid. If the computer’s bid was less than the bid of the partic-

ipant, then the participant would purchase the snack for the

computer’s bid.

Creating sets of high value and low value items. Follow-

ing Zoltak et al. (2018) the program then sorted the food

items based on participants individual WTP. The top and

bottom 7 items were not included, resulting in items ranked

8 to 23 being used as high value items and items ranked 38

to 53 being used as low value items. Then, both the high

value items as the low value items were divided into four sets

of four items, in such a way that the mean value in each set

would be approximately equal. This ensured that the value

differences between high and low value items were matched

for all types of choice trials and that any differences for type

of choices between choice conditions would not be caused by

value differences, but rather the CAT. Two sets of high value

items and two sets of low values items were then selected

as cued items in the following training and the other sets

of high and low value items were selected as uncued items.

This setup enabled us to create 4 sets of choice pairs later

used in the choice task (i.e., cued low, both uncued, cued

high, both cued), see below. For a visual demonstration of

the selection procedure and choice pairs see supplementary

Figure 1.

Cue approach training. The training phase was identical

to that of Zoltak et al. (2018) and lasted around 30 min.

Judgment and Decision Making, Vol. 15, No. 2, March 2020 Training choices toward low value options 257

Figure 1: Overview of main experimental procedure. In the self-paced auction phase (a) participants bid a maximum of

2 euros on each of 60 palatable food items. During the training phase (b) participants performed the CAT, in which they

responded via a button press to items which were paired with auditory cues. During the choice task (c) participants made

choices between two items simultaneously presented on the screen, and their eye gaze was recorded during the whole choice

task. ITI = inter trial interval, GSD = go-signal delay.

Judgment and Decision Making, Vol. 15, No. 2, March 2020 Training choices toward low value options 258

Participants viewed food items that appeared sequentially

on a black background on a computer screen. They were

instructed to press the B button on the keyboard with their

dominant hand when (and only when) they heard a tone (1000

Hz for 0.2s). They were further instructed to press the button

before the image disappeared from the screen. To keep the

task challenging, the tone occurred after a variable delay

after stimulus onset based on a staircase procedure. That is,

the delay between stimulus onset and the tone (i.e., the go-

signal delay, GSD), was initiated at 650 ms and increased by

17 ms if the participant managed to press the button before

the image disappeared and decreased by 50 ms if he or she

did not respond in time. Images appeared on screen one at a

time and around 25% of the food items was paired with the

tone. The entire set of items was presented 8 times, which

means that participants were exposed to all 60 items 8 times

and needed to respond to each of the cued items 8 times.

Choice task. In the subsequent choice task, participants

were simultaneously presented with two food items to the

left and right of a central fixation cross. Each trial they were

asked to choose one of the items (by pressing either the U

(left) or I (right) keyboard button) within 1500 ms of stimulus

onset. Participants were informed that one of the trials would

be selected and they would receive the snack they chose on

the selected trial for actual consumption. If participants did

not respond within the time window a text would appear on

the screen prompting them to choose faster. Those trials

would be repeated later. The inter-trial interval was random

and ranged from 1 to 2 s. After a choice was made a yellow

frame would appear around the item of choosing for 500

ms., as confirmation. On all choice trials, participants made

a choice between a high and a low value item. In total, there

were four trial types that differed in whether the high and

low value item was cued or uncued that were created from

combinations between sets of high and low value item sets

that were used in the training as cued or as uncued items

(see Appendix). The experimental trials consisted of 16 low

cued trials (i.e., cued low value item vs. uncued high value

item) and 16 high cued trials (i.e., uncued low value item

vs. cued high value item). The baseline trial consisted of 16

both uncued trials (i.e., uncued low value item vs. uncued

high value item). We also added 16 both cued trials (i.e.,

cued low value item vs. cued high value item), however these

did not serve as a main experimental baseline. Participants

performed 64 trials in one block, and the trials were then

repeated in a second block to counterbalance the left/right

position of the images. Eye tracking data was recorded

during this phase of the experiment.

Eye tracking procedure. All eye tracking data was gath-

ered using a SMI iView X machine (500Hz sample fre-

quency). Dominant eye was determined for each participant

before calibrating the eye tracker. In order to prepare the eye

tracking data for analysis and determine the areas of fixation

(left, middle, right) several steps were performed on the raw

data.

Data preparation. Following Olsen (2012) we catego-

rized eye-blinks and eye tracking coordinates that fell beyond

the size of the monitor were marked as invalid data points.

Subsequently, a linear interpolation was performed on the

raw x- and y-coordinates. Linear interpolation was only per-

formed if subsequent missing values (following one another)

were shorter than 50 ms. Finally, data were smoothed with

a moving median filter (time steps of 10 ms).

Due to the fact that eye tracking data was gathered during

the choice task and sometimes participants failed to make a

choice in the correct time, trials in which participants chose

too late were excluded from analyses (1.6%). Next, trials in

which more than 25% of the eye-tracking values were miss-

ing (raw data; 15.7%; e.g., due to blinking) were excluded

from analyses. Finally, trials in which no fixations were

detected within the regions of interest (ROI) were excluded

from analyses (17.37%). The trials included in the analyses

contained on average 1.2% (SD = 3.2%) interpolated data

and from which 4.4% of the data within these trials was

excluded.

Fixation identification. Fixations were identified using

a dispersion threshold identification (I-DT) algorithm (Blig-

naut, 2009). The dispersion threshold was set to 1° of the

visual angle (43.48 pixels; distance from eye to screen =

690 mm; screen size: 1920 x 1080 pixels). Gaze data were

counted as fixations when the x- and y-coordinates measured

over at least 100 ms did not exceed the dispersion threshold.

The average x- and y-coordinates of the fixation and the

length of the fixation were recorded.

Classification of fixations. Fixations were classified in

one of three categories defined by a ROI (aligned with the

position of the pictures): left fixation (130 < x < 830 pixels;

290 < y < 790 pixels), right fixation (1090 < x < 1790; 290

< y < 790), and middle fixation (830 < x < 1090; 290 < y

< 790). The average time looked at either of the ROIs was

similar (Mleft = 206 ms; Mright = 202 ms; Mmiddle = 205 ms).

Eye tracker dependent variable. For each trial, pro-

portion of looking time towards the low-valued picture was

calculated by dividing the looking time at the low valued

picture by the total looking time at the left and right picture.

Memory task. In the memory task participants were yet

again presented with all 60 items and asked whether a partic-

ular item was paired with a sound during the training. Items

appeared on a black background in random order, one by

one. The response was self-paced.

Judgment and Decision Making, Vol. 15, No. 2, March 2020 Training choices toward low value options 259

Table 1: Performance in the cue approach training and memory recall task. S.D.’s in parentheses.

Cued Acc Uncued Acc Cued RT (ms) GSD (ms) Memory Acc

72.84% 99.63% 339.78 586.13 71.13%

(4.45%) (0.60%) (102.84) (100.29) (4.53%)

Cued Acc = accuracy on cued trials; Uncued Acc = accuracy on uncued trials;

Cued RT = the mean reaction time on trials when a response was in time;

GSD = go signal delay, the mean go signal delay on all cued trials;

Memory Acc = accuracy in the memory recall task.

2.2 Results

2.2.1 Confirmatory analyses.

Following our preregistration (https://osf.io/39jnj/) we con-

ducted confirmatory analyses on our behavioural data.

Behavioural data. Overall, participants performed well in

the CAT. For their performance in the CAT and the memory

recall task see Table 1.

Choice data. Following our preregistration, we con-

ducted a pair-wise t-test between the proportion of choosing

low value options within our main comparison, that is cued

low compared to uncued low pairs. As predicted, we found a

significant increase in the proportion of choices of low value

items in cued low pairs (M = .234; SE = .037) compared to

both uncued pairs (M = .172; SE = .027); t (46) = 2.415, p

= .02. In addition, we also analysed our data with a two-

sided repeated measures logistic regression, which included

a fixed intercept and fixed effect for condition (cued low vs.

both uncued), which was dummy coded with the level both

uncued as a reference group. The repeated measures nature

of the data was modelled by including a per-participants ran-

dom adjustment to the fixed intercept (“random intercept”).

This analysis also revealed a statistically significant increase

in choices for cued low value items (OR = 1.48, 95% CI =

[1.10, 1.97], Wald Chi-square = 6.819, p = .01, two-sided

repeated measures logistic regression, see Figure 2). This

result replicates the findings of Zoltak et al. (2018), showing

that CAT can indeed increase the odds of choosing low value

items if the low value is cued comparted to when it is uncued.

We also analysed whether participants chose more low

value items when comparing their choices to trials in which

both items were cued, namely cued low compared to both

cued. In this comparison we did not find a significant dif-

ference in choosing low value items (OR = 1.19, 95% CI =

[0.91, 1.55], Wald Chi-square = 1.628, p = .20, two-sided

repeated measures logistic regression). This finding is con-

sistent with previous work (Zoltak et al., 2018).

p = 0.02

0

10

20

30

40

50

Low cued Both uncued High cued Both cued

Pe

rce

nta

ge

of

Ch

oo

sin

g L

ow

Va

lue

Ite

ms (

%)

Figure 2: Choices for low value items over high value ones

across all trials. Significance level reflects the increase in

choosing low value items between the conditions using a pair

wise t-test. Error bars, S.E.

2.2.2 Exploratory analysis on behavioural data.

Following the suggestions of two anonymous reviewers we

added an additional analysis of our data using a multiple

regression approach. Participants’ choices (0 = high valued

item, 1 = low valued item) during the cue approach train-

ing were analysed using a generalized linear mixed model

approach using the “glmer” function of the lme4 package

(Bates, Maechler, Bolker & Walker, 2013) in R (R Core

Team, 2017). The model included a fixed intercept and fixed

effects for condition (high cued vs. low cued vs. both cued and

vs. both uncued) and value difference as a continuous vari-

able (higher values indicating a higher value difference). Ad-

ditionally, interaction terms between condition and value dif-

ference were included. Value difference was centered. The

fixed effect condition was dummy coded with the level “both

uncued” as reference group. The repeated measures nature

of the data was modelled by including a per-participant ran-

dom adjustment to the fixed intercept (“random intercept”).

All p-values were computed using likelihood ratio tests as

Judgment and Decision Making, Vol. 15, No. 2, March 2020 Training choices toward low value options 260

implemented by the function mixed in the package “afex”

(Singmann, Bolker, Westfall & Aust, 2018).

Choice data. Firstly, we found a significant effect of condi-

tion, j2(3) = 39, p < .001, indicating that the degree to which

participants chose the low valued item was different between

conditions. Specifically, we found that participants chose the

low valued item more often in the low cued condition com-

pared to the both uncued condition (estimate = 0.23, SE =

0.06)1, thus replicating the results of our initial preregistered

analysis by indicating that the CAT can influence people to

choose the low valued cued item when pitted against uncued

high valued item. Additionally, participants chose the low

valued item less often in the high cued condition compared

to the both uncued condition (estimate = −0.65, SE = 0.12)2,

indicating that the CAT was successful in influencing choice

when a high valued cued item when pitted against uncued

low valued item. Together, these results suggest that the CAT

was successful in modifying choice behaviour.

We also analysed whether participants chose more low

value items when comparing their choices to trials in which

both items were cued, namely cued low compared to both

cued. In this comparison we did not find a significant dif-

ference in choosing low value items (estimate = −0.13, SE

= 0.11).3 This finding is consistent with our preregistered

analysis and further proves that the both uncued baseline is

the more reliable baseline (Zoltak et al., 2018).

Choice data and value difference. We also explored

whether the CAT effect is related to the value difference

between choice alternatives and whether we can replicate

the original findings of Zoltak et al. (2018) showing that on

the participant level the strength of CAT on choice is nega-

tively correlated with difference in value between the choice

alternatives. In other words, we tested whether smaller dif-

ferences in value between the choice alternatives are related

to stronger CAT effects in the low cued condition compared

to the both uncued baseline. Hence, we selected the low cued

and both uncued trials, and for each participant calculated

the following two scores: (1) the average value difference be-

tween the high value and low value items on these two types

of choice trials (i.e., value difference score); (2) the differ-

ence between the percentage of choosing low value items

on the low cued trials compared to that on the both uncued

trials (i.e., choice difference score). Note that the second

variable is an index of the effectiveness of the CAT. The

larger the choice difference score, the more increase in low

value choices on low cued trials compared to both uncued tri-

als, hence the more effective the CAT is in boosting choices

1z = 3.93, OR = 1.25, 95% CI = [1.12, 1.41], j2(1) = 14.53, p < .001.

2z = −5.63, OR = 0.52, 95% CI = [0.41, 0.65], j2(1) = 31.84 p < .0001.

3z = −1.24, OR = 1.14, 95% CI = [0.92, 1.41], j2(1) = 1.51, p = .22.

for low-value food for an individual. We then performed cor-

relation analyses between the value difference scores and the

choice difference scores with and without excluding univari-

ate outliers (3 SD from sample mean) and bivariate outliers

(Cook’s distance > 4/N). These correlation analyses revealed

a significant negative correlation between the two variables,

r (40) = −.340, p = .03 (including outliers); r (38) = −.398,

p = .01 (excluding outliers). In order to accommodate issues

with non-uniform residuals, we also computed Spearman

rank-order correlations, which produced similar results (rg(40) = −.216, p = .05 including outliers; rg (38) = −.242, p

= .03 excluding outliers). These results show that the effect

of CAT in increasing the odds of choosing low value items

is stronger when the value difference between high and low

value items is smaller when averaging between participants.

Secondly, following the suggestions of an anonymous re-

viewer, we investigated whether we can find the influence

of value difference and CAT effect on an item level. In or-

der to test this, we included value difference, in the form of

a continuous measure, as a fixed intercept and fixed effect,

as well as an interaction term with condition, in our bino-

mial mixed effects model with condition (cued low versus

uncued) described above. We found a significant effect of

value difference (estimate = −2.83, SE = 0.32)4, indicating

that participants became less likely to choose the low valued

item when the difference between items was high. However,

we found no support for the hypothesis that the effect of value

difference on choice was different between conditions, j2(3)

= 6.16, p = .10. Specifically, value difference did not seem

to make CAT more effective when the difference between

items was low in the low cued condition compared to the

both uncued condition (estimate = −2.86, SE = 0.37)5 (see

Figure 3).

Choice and reaction time. Zoltak et al. (2018) previ-

ously demonstrated that participants chose low value items

slower compared to high value items, suggesting that choices

for low value items are not mere mistakes. That is, when par-

ticipants would have accidentally pressed a key associated

with the low value alternative, as a result of learning to re-

spond quickly to those items as a result of CAT, they would

likely choose those items very quickly compared to when

they chose the high value option. The alternative explana-

tion, using the attentional drift diffusion model (aDDM) as

applied to binary choice (e.g., Krajbich et al., 2010), may

predict that participants would take more time to choose low

value options compared to the high value ones, since the

speed with which the decision threshold would have been

reached would be influenced by the value of the particular

item. Hence, thresholds would be reached faster when par-

ticipants would choose high value items, compared to when

they would choose low value ones.

4z = −8.99, OR = 0.10, 95% CI = [0.03, 0.11], j2(1) = 88.05, p < .0001.

5z = −7.76, OR = 0.24, 95% CI = [.027, .127], j2(1) = 2.39, p = .12.

Judgment and Decision Making, Vol. 15, No. 2, March 2020 Training choices toward low value options 261

0.0%

20.0%

40.0%

60.0%

0.0 0.5 1.0 1.5 2.0

Value Difference (in euro)

Pro

ba

bili

ty o

f C

ho

osin

g L

ow

Va

lue

Predicted Probabilities of Choosing Low Value Options

Figure 3: Predicted Probabilities of Choosing Low Value

Options over High Value Options as a Function of Value Dif-

ference between the Options and Condition. Plot shows

marginal effects of interaction terms in model described in

the main text. Shaded regions represent 95% confidence in-

tervals.

In order to test whether we can replicate the previous find-

ings by Zoltak et al. (2018), we analysed our reaction time

data using a linear mixed effects model using the “lmer” func-

tion of the lme4 package (Bates, Maechler, Bolker & Walker,

2013) in R (R core team, 2017). Response times were cen-

tered and log transformed, and our model included a fixed

intercept and fixed effects for condition (high cued vs. low

cued vs. both cued vs. both uncued) and choice (high value vs.

low value). Additionally, we included an interaction term be-

tween condition and choice. The repeated measures nature of

the data was modelled by including a per-participant random

adjustment to the fixed intercept (“random intercept”). All p

values were computed using the S-method, as implemented

by the function mixed in the package “afex” (Singmann et

al., 2018). All post hoc comparisons were done using the

package “emmeans” (Lenth, Singmann, Love, Buerkner &

Herve, 2019) using the Tukey HSD adjustment. we found a

significant effect of condition, F (3, 386.92) = 5.65, p <.001,

a significant effect of choice, F (1, 3895.88) = 24.52, p <

.001, as well as a significant interaction between condition

and choice, F (3, 3866.98) = 3.21, p = .02. However, a fol-

low up Tukey test revealed that only the low cued and high

cued conditions differed significantly, estimate = −.109, z-

ratio = −2.807, p = .03 (see Figure 4). Participants were, on

average, fastest to choose the high value option in the high

cued condition and, on average, slowest to choose the low

value option in the high cued condition and these reaction

times differed significantly, when compared to the low cued

condition.

700

750

800

850

900

Low cued High cued Both cued Both uncued

Conditions

Re

actio

n T

ime

s (

ms)

Choices0 = High Value1 = Low Value

01

Predicted Values of Reaction Times (ms)

Figure 4: Predicted Values of Reaction Times (in millisec-

onds). Red colour represents response times for high value

options and blue colour represents response times for low

value options. Error bars, S.E.

Eye tracker data. First, in order to demonstrate that par-

ticipants looked longer at the item they chose (Krajbich et

al., 2010) and therefore to indirectly validate our eye tracking

measurement, we analysed the proportion of looking time at

the low value item using a linear mixed effects model using

the “lmer” function of the lme4 package (Bates, et al, 2013).

The fixed effects structure included a fixed intercept and

fixed effect for choice. To account for the repeated measures

nature of the data we also included a per-participant random

adjustment to the fixed intercept (‘random intercept’). All p

values were computed using the KR-method, as implemented

by the function mixed in the package “afex” (Singmann et

al., 2018). There was a significant effect of choice, V = .299,

F (1, 2283.56) = 49.66, p < .001, indicating that partici-

pants looked longer at the items they chose, thus replicating

choice gaze patterns established in previous literature (e.g.,

Schonberg et al., 2014).

Next, in order to test our main hypothesis and to see

whether participants looked longer at the low valued item

in the cued low condition compared to the both uncued con-

dition, we analysed the proportion of looking time again us-

ing a linear mixed effects model. The fixed effects structure

included a fixed intercept and fixed effects as well as interac-

tion terms for the two conditions (cued low vs. both uncued)

and value difference. Value difference was centered. To

account for the repeated measures of our data we included

a per-participant random adjustment to the fixed intercept

(‘random intercept’). Following the suggestion of an anony-

mous reviewer we also included reaction times, which have

been log transformed and centered, in the model to control

for their effect on looking times. All p values were computed

using the S-method, as implemented by the function mixed

Judgment and Decision Making, Vol. 15, No. 2, March 2020 Training choices toward low value options 262

Table 2: Average proportion of looking times at the low val-

ued items across all conditions.

Condition Mean S.D. S.E. Median

Both uncued 0.472 (0.346) 0.011 0.448

Low cued 0.463 (0.346) 0.011 0.448

High cued 0.467 (0.348) 0.011 0.449

Both cued 0.462 (0.350) 0.011 0.450

in the package “afex” (Singmann et al., 2018). Post hoc com-

parison were done using the package “emmeans” (Lenth et

al., 2019) using the Tukey HSD adjustment. We did not find

any significant effects of condition (V = .018, F (3, 3871.20)

= .19, p = .90), specifically when conducting a follow up

Tukey test we did not find that participants looked longer at

the low valued item in the low cued condition compared to

the both uncued condition (estimate = .028, z-ratio = .633,

p = .92). We also did not find a significant effect of value

difference (V = .013, F (1, 225.41) = 0.00, p = .96). Thus,

our results did not confirm our main eye-gaze hypothesis.6

For descriptive statistics see Table 2.

3 General Discussion

In line with previous research (Krajbich et al., 2010; Schon-

berg et al., 2014; Veling et al., 2017), and authenticating

participants’ choices as valid preferences, our participants

overall showed a preference for high value food items over

low value food items, with the mean proportion of choices for

low value foods being below 25% in each condition (see Fig-

ure 1). Next, in line with our main behavioural hypothesis,

we showed an increase in the proportion of choices for low

value items over high value items, when the low value item

was cued compared to when both items were uncued. The

effect of CAT in increasing choices for cued low value items

versus uncued high value items was not obtained when com-

pared to the both cued condition. These behavioural results

replicate our previous findings (Zoltak et al., 2018).

With regard to our exploratory analyses, initially we found

that the average value difference between the choice alter-

natives in the low cued versus both uncued condition was

negatively correlated with the effectiveness of CAT. Thus,

CAT appeared more effective to increase the probability of

choosing low value options when the difference in value be-

tween two options was smaller (see also Zoltak et al., 2018).

However, this relation was not found with item-based analy-

ses. More specifically we did not find evidence in our model

that value difference makes CAT more effective when the

difference between items was low in the low cued condition

6Excluding RTs from the model led to the same conclusions.

compared to the both uncued condition. Due to these incon-

clusive results, we refrain from speculating on the role that

value difference plays in CAT effectiveness. More research

is needed to determine whether CAT may be more suited to

influence choices for low over high items for a) participants

who tend to have relatively small value differences between

the bid items to start with, and/or b) participants who tend to

generally bid within a narrow spread, which could indicate

a general lack of strong preferences.

Second, with regard to the response time analysis, we

found that response times differ as a function of choice and

condition, and that participants are on average slower when

choosing low value options compared to high value options

(see Figure 3). However, this difference was statistically

significant only between the low cued and high cued condi-

tion. The fact that participants were fastest to choose high

value options in the high cued condition is not surprising, as

CAT may have boosted the participants natural preference

and choice of the high value options, therefore providing us

with a limited number of observations (and reaction times)

in which they actually choose a low value item. Therefore,

further research is needed to see whether there is indeed a

robust difference in response time between cued low and

high value options.

With regards to our eye-tracking analysis we replicated

previous findings (e.g., Krajbich et al., 2010; Krajbich &

Rangel, 2011; Schonberg et al., 2014) showing that partici-

pants spend more time fixating on the chosen item compared

to the unchosen alternative. To test whether CAT directs at-

tention towards cued low value options that are pitted against

uncued high value ones, we additionally compared the pro-

portions of looking time at low value options between cued

low and both uncued trials. Interestingly, and against our

hypothesis and previous suggestion (Zoltak et al., 2018), we

found no evidence that the behavioural effect on choice can

be explained by enhanced attention to cued items. We now

discuss two possible reasons for the absence of this effect.

First, it could be that our measurement of the gaze patterns

was not sensitive enough to pick up any meaningful gaze

patterns because of the cuing manipulation. Note, however,

that the eye-tracker did show differences between gazes to-

ward chosen (mostly high value) versus unchosen (mostly

low value) items, suggesting that the equipment was sen-

sitive to detect differences in participants’ gaze patterns in

a meaningful way. Nonetheless, we examined visual atten-

tion toward low value items presented near high value items,

which may weaken any effects of cuing on visual attention

compared to when two items of equal value are presented

as was done in the previous study (Schonberg et al., 2014).

In other words, it may be easier to detect effects of visual

attention as a function of food value or food choice than as

a function of cuing, especially when two food items differ in

value.

Judgment and Decision Making, Vol. 15, No. 2, March 2020 Training choices toward low value options 263

Second, although the effect of CAT on choices has been

shown repeatedly (e.g., Bakkour et al., 2016; Schonberg et

al., 2014; Veling, Chen, et al., 2017), to date only a few ex-

periments examined whether CAT increases visual attention

for cued over uncued items (Salomon et al., 2019; Schon-

berg et al., 2014), and only one experiment showed this

effect (Schonberg et al., 2014). Thus, it could also be that

this initially reported effect of CAT on visual attention is not

so robust as the behavioural effect. This could mean that

there may be additional mechanisms that could explain how

CAT changes choices. For instance, participants may have

learned a response tendency to quickly react to the cued low

value items, which could sometimes influence choices for

consumption. Although previous work suggests effects of

CAT cannot be explained by creation of low level stimulus-

response links (e.g., CAT also influences choices when peo-

ple make choices with their eyes instead of their fingers;

Schonberg et al., 2014; see also Bakkour et al., 2016), it

is possible that people learn a strong tendency to select the

cued items during CAT, which may subsequently influence

their decisions for these items irrespective of whether they

look at them. However, there is no indication in our data that

people are quicker to select low value items that are cued

compared to when these items are not cued, which would be

predicted by this account. The present research thus raises

new questions regarding how CAT influences choices for low

value items.

Interestingly, a recent article published by Salomon et al.

(2019) has also failed to replicate the original findings of

Schonberg et al. (2014), showing that participants did not

fixate more on cued stimuli compared to uncued stimuli. In

their additional exploratory analysis of gaze patterns during

the cue-approach training they found a developing gaze-bias

pattern, which manifested itself by longer fixations on the

cued stimuli compared with the uncued stimuli during the

training. Yet, this difference was not correlated with subse-

quent choices in the choice task.

To conclude, we have shown that CAT can increase the

probability of choosing low value items when people make

choices between low and high value items. However, we

found no evidence that this behavioural effect can be ex-

plained through enhanced visual attention for cued items.

More work is thus needed to examine how CAT changes the

probability of choosing low value items.

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Appendix

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Willingness to Pay (WTP) Sorting Procedure and Examples of Choice Pairs. Left panel represents the selection procedure

based on WTP. Right panel demonstrates all pairs used in the choice task. For each pair (e.g., cued low) every high value

item was paired with every low value item.